As the need and advancements of various kinds of analysis and research using sensitive personal data are becoming more and more popular and significant, the risk of privacy violations is also increasing manifold. One of the most successful proposals to address the issue of privacy protection is differential privacy (DP)[1]}, [2]}, a mathematical property that makes it difficult for an attacker to detect the presence of a record in a dataset, and which is typically achieved by adding noise to the results of queries performed on the dataset. With advancements in research, the local variation of differential privacy (LDP)[3]} has gained substantial popularity in the community, thanks to the fact that the noise is applied at the user's end and therefore it does not need a trusted curator. The local model of privacy is particularly suitable for the situations in which the user communicates her personal data in exchange for some service. One such scenario is the use of location-based services (LBS), where the user typically sends her location in exchange for information like the shortest path to a destination, points of interest in the surroundings, traffic information, friends nearby, etc. The local model of differential privacy can be implemented directly on the user's device (tablet, smartphone, etc.) and the fact that the user can control directly her privacy-protection mechanism is very appealing. However, the drawback of injecting noise locally to the datum is that it decreases also the utility from the perspective of the user (besides that of the data collector) because the quality of service (QoS) is usually affected negatively by the lack of accuracy of the data. Therefore, in the local model of privacy protection, taking into account the utility of the data both on the users' and the service providers' sides is essential.
